Introduction: This analysis performs a gene-gene clustering procedure that will identify clusters of co-expressed genes across multiple sample groups. It first runs an ANOVA to find genes significantly changed across sample groups and uses these genes as seeds to initiate a number of gene clusters. These clusters will be further refined based on several user-specific paramters. Gene set enrichment analysis is then used to find pre-defined gene sets that are over-represented in each cluster.
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Perturbed rhythmic activation of signaling pathways in mice deficient for Sterol Carrier Protein 2-dependent diurnal lipid transport and metabolism. (GSE67426)
Jouffe C, Gobet C, Martin E, Métairon S et al. Perturbed rhythmic activation of signaling pathways in mice deficient for Sterol Carrier Protein 2-dependent diurnal lipid transport and metabolism. Sci Rep 2016 Apr 21;6:24631. PMID: 27097688.
Comparison of liver mRNA expression from Scp2 KO and wild-type mice harvested every 2 hours during 3 consecutive days.
Circadian rhythm in mouse liver, wild type mice only, 3 replicates every 2 hours (12 groups). Each gene was adjusted to its 0 hour mean and rescaled to make its standard deviation equal to 0. As a demonstration, only a subset of genes in the original data with high between sample variance were used.
The input data matrix was normalized using sample group WT_00Hr as control, so, the data of each gene was substracted by control group mean and had SD equal to 1.0
Summary statistics and ANOVA p value across all sample groups were calculated for each gene.
Differentially expressed genes (DEGs) were selected as seeds for generating gene clusters, using the following criteria:
A total of 232 genes were selected. These genes would be used as seeds to generate gene clusters in the next step.
Gene clusters were identified from the DEG seeds with the following steps:
8 gene clusters of 221 genes were identified from 232 seed DEGs.
The gene clusters identified from the DEG seeds were further refined with the following steps:
The reclustering didn’t converge after 20 cycles
A total of 554 genes were clustered after refinement.
More info:
| Cluster | Num_Gene | Mean_WT_00Hr | Mean_WT_02Hr | Mean_WT_04Hr | Mean_WT_06Hr | Mean_WT_08Hr | Mean_WT_10Hr | Mean_WT_12Hr | Mean_WT_14Hr | Mean_WT_16Hr | Mean_WT_18Hr | Mean_WT_20Hr | Mean_WT_22Hr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cluster_1 | 74 | 0 | 0.320 | 0.4003 | 0.4754 | -0.0674 | -0.8074 | -1.3232 | -1.7725 | -1.4775 | -0.8675 | -0.4452 | 0.038 |
| Cluster_2 | 92 | 0 | 0.220 | 0.6953 | 1.1131 | 1.3442 | 0.7731 | 0.5681 | -0.5761 | -1.1411 | -0.8391 | -0.4592 | -0.140 |
| Cluster_3 | 76 | 0 | 0.027 | 0.4720 | 0.9529 | 1.6852 | 1.7297 | 1.8217 | 1.0513 | 0.1103 | -0.0200 | -0.1501 | -0.150 |
| Cluster_4 | 68 | 0 | -0.110 | -0.2174 | 0.0500 | 0.8026 | 1.5266 | 1.9326 | 1.9415 | 1.4404 | 1.0700 | 0.7313 | 0.170 |
| Cluster_5 | 22 | 0 | -0.910 | -1.3292 | -1.3408 | -1.3086 | -0.8028 | -0.5605 | 1.1441 | -0.4487 | -0.9260 | -1.4410 | -0.760 |
| Cluster_6 | 41 | 0 | 0.120 | -0.3093 | -1.0027 | -1.6774 | -1.6972 | -1.4500 | -0.3144 | 0.4795 | 0.2814 | -0.7223 | -0.760 |
| Cluster_7 | 81 | 0 | -0.360 | -0.7090 | -0.8816 | -1.0076 | -0.4168 | -0.1783 | 0.7138 | 1.1981 | 1.1456 | 0.8200 | 0.290 |
| Cluster_8 | 100 | 0 | -0.110 | -0.3746 | -0.7491 | -1.5633 | -1.8686 | -1.9389 | -1.6773 | -0.7202 | -0.1745 | 0.1049 | 0.150 |
Find predefined gene sets enriched in gene cluster comparing to the background.
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Check out the RoCA home page for more information.
To reproduce this report:
Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory
To generate a new report using your own input data and parameter, edit the following items in the YAML file:
Run the code below within R Console or RStudio, preferablly with a new R session:
if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }
CreateReport(filename.yaml); # filename.yaml is the YAML file you just downloaded and edited for your analysis
If there is no complaint, go to the output folder and open the index.html file to view report.
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] splines stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] CHOPseq_0.0.0.9000 Agri_0.0.0.9000 edgeR_3.10.2
## [4] limma_3.26.9 NOISeq_2.16.0 GenomicRanges_1.22.4
## [7] GenomeInfoDb_1.6.3 IRanges_2.4.8 S4Vectors_0.8.11
## [10] Biobase_2.28.0 BiocGenerics_0.16.1 Matrix_1.2-2
## [13] vioplot_0.2 sm_2.2-5.4 rchive_0.0.0.9000
## [16] htmlwidgets_0.5 DT_0.1 GtUtility_0.0.0.9000
## [19] gplots_3.0.1 awsomics_0.0.0.9000 yaml_2.1.13
## [22] rmarkdown_0.9.6 knitr_1.12.3 RoCA_0.0.0.9000
## [25] RCurl_1.95-4.8 bitops_1.0-6 devtools_1.11.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.4 XVector_0.10.0 formatR_1.3
## [4] highr_0.5.1 zlibbioc_1.14.0 tools_3.2.2
## [7] digest_0.6.9 lattice_0.20-33 jsonlite_0.9.20
## [10] evaluate_0.9 memoise_1.0.0 RSQLite_1.0.0
## [13] DBI_0.3.1 withr_1.0.1 stringr_1.0.0
## [16] gtools_3.5.0 caTools_1.17.1 grid_3.2.2
## [19] AnnotationDbi_1.32.3 gdata_2.17.0 magrittr_1.5
## [22] htmltools_0.3.5 KernSmooth_2.23-15 stringi_1.0-1
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